The reconstruction of 3D city models has garnered significant interest in recent years. However, the majority of existing reconstruction methods primarily focus on LOD2 models, while LOD3 model reconstruction often relies on manual labor, and the primary data sources are street view images. This research aims to advance this field by reconstructing LOD3 models through the addition of windows and doors to existing LOD2 models, thereby maximizing the utility of available 3D building models, as well as the accurate addition of windows and doors. This research innovatively utilizes aerial oblique images as the data source for extracting building openings and employs 3D BAG LOD2.2 models as the basic 3D building structures. The 3D facades are projected onto the 2D aerial image space using perspective projection and registration is employed on the projection facade and oblique aerial images. Subsequently, Mask R-CNN is employed to detect and extract the building openings from these projections. Following the extraction, the layout of the openings within the same facade is optimized in terms of both size and position. Lastly, the relative positions of the openings on the facade images are combined with the 3D coordinates of the corresponding facade to calculate the positions of the openings in 3D space. This information is then integrated into the LOD3 model, resulting in a more detailed and accurate representation of the buildings.
This approach successfully reconstructs the final LOD3 model in CityJSON format, which passes the val3dity validation. By effectively utilizing existing 3D building models, this approach conserves a considerable amount of computational resources required for reconstruction. The simplicity and high level of automation of this approach make it a promising solution for reconstructing large-scale LOD3 buildings, leading to more accurate and detailed large 3D urban models.

A 3D city model uses three-dimensional geometries to represent and model urban environments, in which the building model is the key feature. With the development of computer and data collection technologies, 3D city models are gaining growing capacities regarding storing rich information. This makes 3D city models more potentially useful than ever in the urban application domain.

When implementing 3D city models, the CityGML model is currently the most frequently used standard. It is being used by cities all over the world. CityJSON is an encoding for a subset of the OGC CityGML data model. It is a JSON-based data exchange format for digital 3D models of cities and landscapes, and it is easy for various operations. Due to the uniqueness of its structure, it is necessary to design corresponding data models to store CityJSON files in the database, to make querying and updating data within the database easy and convenient.

There are already some open-source solutions for storing CityJSON files in the database, 3DCityDB is one of them. 3DCityDB can store, manage and visualize data well, and it is open source. But the database design is very complex: for a tile of 3D BAG data, 3DcityDB uses a total of 66 tables to store data. The structure of the data model results in difficulties for database users to understand the imported data, and potentially leads to non-optimal operation performance when retrieving data for urban applications. Based on the drawbacks of the existing DBMS when dealing with the CityJSON data format, this project aims to develop a Postgres data model that can store CityJSON files simply and efficiently. The developed Postgres data model (CJDB) has a simpler table structure and data model design, a CityJSON data importer, and an interactive API user interface.

After going through this document (data model, importer and API section), the potential users will have the ability to:

• Import CityJSON files into a Postgres database,
• Perform queries on imported data,
• Perform operations using CJDB API.

In addition, by reading the benchmarking section, the users can gain an overview of the CJDB’s performance over the 3DcityDB’s.

The CJDB project is open-sourced, available on GitHub page.

The CJDB project will be potentially further developed by 3D geoinformation group of TU Delft and 3DGI.